Walgreens SDE III Interview Questions

Table Of Contents

• Coding and Problem-Solving Questions
• Data Structures and Algorithms Questions
• System Design and Architecture Questions
• Testing and Debugging Questions
• Behavioral and Scenario-Based Questions

Landing a Walgreens SDE III position is a powerful step in any software engineer’s career, and it demands a thorough understanding of advanced coding principles, system architecture, and problem-solving skills. When preparing for this interview, I know it’s crucial to be ready for in-depth questions on data structures, algorithms, and scalable system design that reflect the technical demands at Walgreens. They typically expect proficiency in Java, Python, and JavaScript, alongside a solid grasp of real-world project management and agile methodologies. These interviews push candidates to not only solve complex problems but also to showcase how they approach design thinking, optimization, and teamwork—all essential for making impactful contributions at Walgreens.

In this guide, I’ve compiled essential insights and practice questions tailored to the Walgreens SDE III role. By diving into areas such as coding, system design, and integration strategies, I can sharpen my skills to meet Walgreens’ high standards. For those aiming for a competitive edge, this guide will help build confidence and readiness to tackle challenging technical and scenario-based questions. With average salaries for Walgreens SDE III roles ranging between $130,000 and $160,000, this is an opportunity worth preparing for with precision and dedication.

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Coding and Problem-Solving Questions

1. How would you find the largest subarray with a given sum in an array of integers? Discuss time complexity.

To find the largest subarray with a given sum in an integer array, I would use a hashmap to store cumulative sums at different points in the array. By using this approach, I can track where certain sums have occurred before. This allows me to quickly calculate if a subarray has the desired sum by checking if the cumulative sum minus the target sum has appeared before in the hashmap. Each time I find such a pair, I can determine the length of the subarray, and I keep track of the largest one found. This method significantly reduces complexity compared to a brute-force approach.

The time complexity of this algorithm is O(n)O(n)O(n), as it only requires a single pass through the array while updating and checking the hashmap. The space complexity is also O(n)O(n)O(n) due to the hashmap, which holds cumulative sums up to the current index. The alternative brute-force solution, which checks all subarrays, has a complexity of O(n2)O(n^2)O(n2), making this optimized approach preferable for larger arrays. Here’s a simple code snippet:

def largest_subarray_with_sum(arr, target_sum):
    sum_map = {}
    current_sum = 0
    max_len = 0
    for i in range(len(arr)):
        current_sum += arr[i]
        if current_sum == target_sum:
            max_len = i + 1
        if current_sum - target_sum in sum_map:
            max_len = max(max_len, i - sum_map[current_sum - target_sum])
        if current_sum not in sum_map:
            sum_map[current_sum] = i
    return max_len

This code efficiently identifies the longest subarray with a given sum by calculating cumulative sums and using the hashmap to track required sums, resulting in faster processing times.

2. Can you write a function to detect cycles in a linked list? Explain the approach you’d take.

To detect cycles in a linked list, I would employ the Floyd’s Cycle Detection Algorithm, also known as the tortoise and hare algorithm. This method involves two pointers: one that moves one node at a time (the tortoise) and another that moves two nodes at a time (the hare). If there’s a cycle in the linked list, the two pointers will eventually meet within the cycle, indicating a loop exists. If they don’t meet and the hare pointer reaches the end of the list, it confirms there’s no cycle.

This approach is efficient because it only requires O(n)O(n)O(n) time complexity, where nnn is the number of nodes in the list, and O(1)O(1)O(1) space complexity since it doesn’t require additional data structures. Using two pointers keeps the algorithm lightweight, as it doesn’t need to store visited nodes. Here’s a simple code example of this approach:

class ListNode:
    def __init__(self, value=0, next=None):
        self.value = value
        self.next = next

def has_cycle(head):
    slow, fast = head, head
    while fast and fast.next:
        slow = slow.next
        fast = fast.next.next
        if slow == fast:
            return True
    return False

In this code, the pointers slow and fast start from the head of the list. If they meet, we detect a cycle. This solution is efficient, using minimal memory and running in linear time, making it ideal for cycle detection in linked lists.

See also: Collections in Java interview Questions

3. Given a string, create an algorithm to find the longest substring without repeating characters.

To find the longest substring without repeating characters in a string, I would use a sliding window approach, which involves expanding and contracting a window across the string while keeping track of characters within it. Starting with an empty window, I expand it by adding characters from the string until a repeat character is found. At that point, I shrink the window from the start until there are no repeated characters, and I continue expanding again. Throughout this process, I track the maximum length of the substring encountered without any repeated characters.

For better efficiency, I use a hashmap to store the index of each character, which allows me to update the starting position of the sliding window whenever a duplicate character is found. This approach has a time complexity of O(n)O(n)O(n), where nnn is the length of the string, as each character is processed only once in the hashmap. Here’s the code for this algorithm:

def longest_substring_without_repeating(s):
    char_index = {}
    start = max_len = 0
    for i, char in enumerate(s):
        if char in char_index and char_index[char] >= start:
            start = char_index[char] + 1
        char_index[char] = i
        max_len = max(max_len, i - start + 1)
    return max_len

In this function, I keep track of character positions in char_index, and if a character is repeated within the window, I adjust start accordingly. This code efficiently finds the length of the longest substring without repeating characters, making it ideal for string manipulation problems.

4. How would you optimize finding the kth largest element in an unsorted array?

To find the kth largest element in an unsorted array, I would use the Quickselect algorithm, which is based on the same partitioning technique as QuickSort. Instead of fully sorting the array, Quickselect allows us to partition the array such that the kth largest element is in its correct position. This method has an average time complexity of O(n)O(n)O(n), which is efficient for large datasets compared to fully sorting the array, which has a time complexity of O(nlog⁡n)O(n \log n)O(nlogn).

Another effective approach is to use a min-heap of size kkk. I insert elements from the array into the heap until it reaches size kkk; after that, I only keep the largest elements by removing smaller elements as necessary. This way, the root of the min-heap always contains the kth largest element. This approach has a time complexity of O(nlog⁡k)O(n \log k)O(nlogk), which is suitable for cases where k is much smaller than n. Here’s a code snippet implementing the min-heap method:

import heapq

def kth_largest(nums, k):
    min_heap = nums[:k]
    heapq.heapify(min_heap)
    for num in nums[k:]:
        if num > min_heap[0]:
            heapq.heapreplace(min_heap, num)
    return min_heap[0]

By building the min-heap and maintaining only the k largest elements, I can efficiently find the kth largest element without sorting the entire array. This approach optimizes both time and space, making it highly practical for large data arrays.

See also: Accenture Java Interview Questions and Answers

5. Write code to implement a binary search algorithm. How can you modify it to work on rotated sorted arrays?

The binary search algorithm is one of the most efficient search algorithms with a time complexity of O(log⁡n)O(\log n)O(logn), making it ideal for sorted data. In binary search, I would start by dividing the sorted array into two halves, checking if the target element is in the middle. If it isn’t, I determine whether to continue the search in the left or right half based on the target’s value relative to the middle element. This process is repeated until the target is found or the search space is empty. Here’s a basic binary search implementation:

def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1

To modify binary search for a rotated sorted array, I first determine which half of the array is sorted. By checking if the middle element is greater than or less than the left and right bounds, I can determine if the rotation point lies to the left or right. I then decide which half to search based on the sorted side of the array. This allows me to maintain O(log⁡n)O(\log n)O(logn) efficiency even in a rotated sorted array.

Data Structures and Algorithms Questions

6. Describe an efficient approach to balance a binary search tree (BST).

To balance a binary search tree (BST) efficiently, I would convert the BST into an AVL tree or a Red-Black tree. An AVL tree is a self-balancing BST where the difference between heights of left and right subtrees for any node is at most one. Each time a node is inserted or deleted, I would check the balance factor of each node and apply rotations to maintain balance. The AVL tree operations ensure O(log⁡n)O(\log n)O(logn) time complexity for insertions, deletions, and lookups.

For rebalancing, I’d use four types of rotations: left rotation, right rotation, left-right rotation, and right-left rotation. A rotation effectively shifts nodes to balance the tree while keeping the BST properties intact. Here’s a basic example of a left rotation used for balancing an AVL tree:

class TreeNode:
    def __init__(self, value=0, left=None, right=None):
        self.value = value
        self.left = left
        self.right = right
        self.height = 1

def left_rotate(z):
    y = z.right
    T2 = y.left
    y.left = z
    z.right = T2
    z.height = 1 + max(get_height(z.left), get_height(z.right))
    y.height = 1 + max(get_height(y.left), get_height(y.right))
    return y

Balancing using AVL trees maintains the height of the tree, which helps ensure optimal performance for search, insert, and delete operations.

See also: Arrays in Java interview Questions and Answers

7. Explain how you would implement a Least Recently Used (LRU) cache. What data structures would you use?

To implement an LRU cache, I would use a combination of a doubly linked list and a hash map. The doubly linked list maintains the order of elements based on usage, with the most recently accessed items at the front. The hash map provides constant-time access to each cache item. When a new item is accessed or added, it’s moved to the front of the list. If the cache reaches its maximum size, I would remove the least recently used item from the end of the list.

In this setup, the doubly linked list allows constant-time addition and deletion from both ends, while the hash map allows O(1)O(1)O(1) access to any item. Here’s a Python implementation outline:

class Node:
    def __init__(self, key, value):
        self.key = key
        self.value = value
        self.prev = self.next = None

class LRUCache:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}
        self.head, self.tail = Node(0, 0), Node(0, 0)
        self.head.next, self.tail.prev = self.tail, self.head

    def _remove(self, node):
        prev, nxt = node.prev, node.next
        prev.next, nxt.prev = nxt, prev

    def _add(self, node):
        node.prev, node.next = self.head, self.head.next
        self.head.next.prev = node
        self.head.next = node

    def get(self, key):
        if key in self.cache:
            node = self.cache[key]
            self._remove(node)
            self._add(node)
            return node.value
        return -1

    def put(self, key, value):
        if key in self.cache:
            self._remove(self.cache[key])
        node = Node(key, value)
        self._add(node)
        self.cache[key] = node
        if len(self.cache) > self.capacity:
            lru = self.tail.prev
            self._remove(lru)
            del self.cache[lru.key]

This implementation allows for constant-time access and maintains the order of recently used items, making it effective for caching.

8. Can you design an algorithm to merge k sorted linked lists? Explain the time complexity.

To merge k sorted linked lists, I would use a min-heap (or priority queue). Each list’s head node is inserted into the heap, which helps to efficiently select the smallest element among all lists. The smallest node is extracted from the heap and added to the merged list, and then the next node from the same list is inserted into the heap. This process is repeated until all nodes have been merged.

Using a min-heap, each node insertion and deletion in the heap takes O(log⁡k)O(\log k)O(logk) time, where kkk is the number of lists. Given that we’re processing nnn nodes in total, the time complexity for this algorithm is O(nlog⁡k)O(n \log k)O(nlogk). Here’s a code example using Python’s heapq:

import heapq

class ListNode:
    def __init__(self, value=0, next=None):
        self.value = value
        self.next = next

def merge_k_lists(lists):
    min_heap = []
    for index, node in enumerate(lists):
        if node:
            heapq.heappush(min_heap, (node.value, index, node))
    
    dummy = ListNode()
    current = dummy

    while min_heap:
        value, index, node = heapq.heappop(min_heap)
        current.next = node
        current = current.next
        if node.next:
            heapq.heappush(min_heap, (node.next.value, index, node.next))

    return dummy.next

This approach is efficient and maintains a balanced runtime, allowing for rapid merging of multiple sorted linked lists.

9. How would you handle duplicate values in a hash map while maintaining constant-time access?

To handle duplicate values in a hash map while maintaining constant-time access, I would use a hash map of lists or sets. When duplicate values are possible, the map key would store a list or set of values instead of a single value. This structure allows me to store all duplicate values under the same key, maintaining efficient insertion and retrieval.

If only certain duplicates need handling (e.g., unique values or latest timestamps), I would use additional checks or data structures (such as timestamped entries or sorted lists) to retrieve the needed data efficiently. This approach retains the O(1)O(1)O(1) access time for typical operations since accessing an element by key remains fast, and handling duplicates within each key’s list is straightforward. For example:

hash_map = {}
def add_to_map(key, value):
    if key in hash_map:
        hash_map[key].append(value)
    else:
        hash_map[key] = [value]

def get_values(key):
    return hash_map.get(key, [])

This approach preserves the hash map’s structure while allowing storage of multiple values per key, providing efficient data retrieval and storage.

See also: Java Interview Questions for Freshers Part 1

10. Describe the difference between BFS and DFS. When would you prefer one over the other?

Breadth-First Search (BFS) and Depth-First Search (DFS) are two primary traversal techniques used on graphs or trees. BFS explores all nodes at the present depth before moving to nodes at the next depth level. This makes BFS ideal for finding the shortest path in unweighted graphs and is well-suited for algorithms requiring level-wise traversal. BFS generally uses a queue, which supports O(n)O(n)O(n) time complexity for traversal.

On the other hand, DFS dives deep into one branch of the tree or graph before backtracking, making it suitable for pathfinding and exploring deep structures. DFS is efficient for detecting cycles in graphs, solving maze problems, and scenarios where all nodes must be visited. DFS can be implemented using recursion or a stack, and like BFS, it has a O(n)O(n)O(n) time complexity. However, DFS can be memory efficient in specific cases, especially with recursion, as it doesn’t require storing all nodes in memory simultaneously like BFS does.

I would choose BFS for shortest path problems and DFS when depth exploration or cycle detection is required, tailoring each to the problem’s structure.

System Design and Architecture Questions

11. Design a scalable URL shortening service (like TinyURL). What components would you include, and how would they interact?

For a scalable URL shortening service like TinyURL, I’d design a system with several key components: API Gateway, Hashing or Encoding Service, Database, and Cache. The API Gateway would handle incoming requests for shortening URLs and redirecting to the original URLs. To create unique short URLs, I would use a hashing algorithm or a base conversion technique to convert the long URL into a short one, ensuring that each short URL is unique.

In terms of storage, I’d use a NoSQL database like DynamoDB or Cassandra to store mappings between original and shortened URLs, as they can handle large volumes of data with minimal latency. Additionally, a caching layer using Redis or Memcached would store frequently accessed URLs to reduce database load and improve response times. The service would also need a monitoring tool to track performance metrics and a load balancer to distribute requests efficiently.

12. Explain how you would design a real-time notification system for a large user base. How would you ensure low latency?

For a real-time notification system at scale, I would first introduce a message broker like Kafka or RabbitMQ to queue notifications and handle high throughput. The message broker would separate the notification generation logic from the notification delivery, ensuring a smooth flow even under heavy load. Notifications would be delivered using a push architecture for real-time updates, and WebSockets could provide low-latency connections, allowing the server to push messages directly to users’ devices.

To further ensure low latency, I’d use multiple delivery channels, such as SMS, email, and in-app notifications, with priority-based routing to ensure urgent messages are delivered promptly. To manage high concurrency, I’d also implement a load balancer and distributed caching. Finally, using edge servers in different regions would help reduce latency by bringing notifications closer to users.

See also: React JS Props and State Interview Questions

13. How would you design an API rate limiter to handle millions of requests per second?

For an API rate limiter that supports millions of requests per second, I would deploy a distributed system with load balancing and a global rate-limiting service. The rate limiter would track requests per user or API key using distributed counters stored in a fast, in-memory data store like Redis. Each user’s request count would be maintained with an expiration time that resets periodically (e.g., per minute or hour).

A leaky bucket or token bucket algorithm would help enforce the rate limit, allowing a certain number of requests within a given time frame while rejecting excessive requests. To handle the distributed scale, I’d ensure data replication and sharding for counters, with Redis nodes distributed across multiple regions to provide high availability and avoid bottlenecks. Additionally, caching frequently requested limits in local memory would reduce Redis queries, helping achieve a stable, high-performance rate-limiting system.

14. Describe a strategy to scale a relational database for high read and write operations. What trade-offs would you consider?

To scale a relational database under heavy read and write loads, I would implement database replication and partitioning. For read scaling, setting up read replicas allows multiple nodes to handle read requests, thus offloading the primary database. I’d direct write operations to a primary database and use replication to sync data across read replicas, keeping data consistent for read operations.

For write scaling, horizontal partitioning (or sharding) distributes data across multiple database instances, allowing for simultaneous writes on different shards. Although this approach increases storage and processing capacity, it introduces complexity in handling transactions across shards and data consistency. To balance performance and complexity, I’d evaluate which queries need immediate consistency and prioritize those in a single shard while allowing eventual consistency for others. Choosing between these trade-offs depends on the application’s tolerance for slight delays and consistency requirements.

15. Walk us through your approach to design a chat application that supports large-scale, real-time messaging.

To design a large-scale real-time chat application, I’d first implement a WebSocket server to support persistent connections, allowing real-time message delivery without frequent reconnections. Each user connection would be handled by a load balancer to evenly distribute traffic across WebSocket servers, ensuring high availability and reliability. The messages themselves would be routed through a message broker like Kafka, which ensures that each message reaches its intended recipient while allowing for asynchronous handling.

For data storage, I’d use a NoSQL database to handle user data, message logs, and metadata due to its ability to scale horizontally. To support features like offline messaging, I’d implement a caching layer with Redis to temporarily store undelivered messages, which can be forwarded when the user reconnects. For scalability, horizontal scaling would be essential, with multiple instances of WebSocket and storage servers deployed across regions to minimize latency for users across different geographical locations.

Testing and Debugging Questions

16. How would you write unit tests for a RESTful API? What scenarios would you test?

To write unit tests for a RESTful API, I would first set up a testing framework like JUnit or Pytest, depending on the programming language. I’d use mocking tools like Mockito or Mock Server to simulate dependencies (e.g., database connections or external services) so the tests can focus solely on the API endpoints themselves. I’d structure the tests to verify core functionalities of each endpoint, including request handling, response formatting, and error management.

The scenarios I’d cover include status code checks to confirm that each endpoint returns the expected HTTP status (e.g., 200 for successful GET, 201 for POST creation, 400 for invalid data). I’d also test response data integrity, ensuring the API returns the correct data format and structure. Error scenarios are also critical, so I’d test how the API handles missing fields, invalid data types, unauthorized access, and server errors. Additionally, boundary conditions like empty inputs and maximum payloads would be included to ensure robustness under varied inputs.

See also: TCS AngularJS Developer Interview Questions

17. Describe your approach to debugging memory leaks in a large-scale application. What tools would you use?

For debugging memory leaks in a large-scale application, I’d first identify which components show increasing memory usage over time. I’d use profiling tools like VisualVM for Java applications or Valgrind for C/C++ to monitor memory allocation and spot patterns or objects that aren’t released as expected. Garbage collection logs and analysis can also help identify memory leaks in languages like Java by revealing objects that remain active without being referenced.

I’d then isolate the specific functions or components causing the leak by running memory snapshots or heap dumps. Heap dump analysis can show exactly where objects are held in memory, helping identify the cause of the leak. Additionally, I’d use automated memory leak detection tools such as LeakCanary (for Android) or AppDynamics (for web applications) to automate memory tracking and find problematic patterns. My goal would be to optimize memory allocation and, if possible, adjust the code to ensure objects are disposed of correctly after use.

18. How would you test a distributed system for performance and reliability under load?

To test a distributed system for performance and reliability under load, I’d start with load testing using tools like Apache JMeter or Gatling to simulate high traffic and measure response times. This testing would help determine how the system handles concurrent requests and highlight any bottlenecks. For reliability, I’d include stress testing to push the system beyond its expected load limits, which helps identify breaking points and areas of improvement under extreme conditions.

For distributed systems, failure simulation is also crucial. I’d use tools like Chaos Monkey or Gremlin to simulate different types of failures (e.g., node crashes, network latency, or server outages). This ensures that the system maintains functionality or degrades gracefully under adverse conditions. Monitoring tools such as Prometheus or Grafana would track system health metrics during these tests, allowing me to analyze performance trends and pinpoint issues in response times, CPU, memory usage, and network I/O.

See also: TCS Java Interview Questions

Behavioral and Scenario-Based Questions

19. Can you discuss a time when you had to resolve a conflict in a team project? How did you handle it, and what was the outcome?

In a previous team project, we encountered a conflict when there were differing opinions on the best approach to implement a key feature. While some team members preferred a straightforward, fast-to-develop solution, others, including myself, advocated for a more robust design that would require additional time upfront but provide better scalability and maintainability in the long run. This difference in priorities led to a standstill, affecting our team’s productivity.

To resolve this, I first facilitated a meeting where each team member could share their concerns and reasoning openly. After everyone had a chance to speak, I proposed a compromise: we could start with the quicker approach but add comments and plan for future improvements in our backlog. This way, we could meet our current deadline while acknowledging the need for enhancements. This approach allowed us to move forward, and over time, we revisited and optimized that feature, achieving both short-term and long-term goals. The resolution not only improved our team dynamics but also instilled a culture of open communication and compromise.

20. Describe a project where you were responsible for optimizing code performance. What steps did you take, and what was the result?

In one project, I was tasked with improving the performance of a data-processing pipeline that had grown slower as data volumes increased. The pipeline processed large datasets, but we faced challenges with inefficient database queries and redundant computations that delayed results. My first step was to identify bottlenecks by using profiling tools, which pointed out specific queries and functions consuming excessive resources.

I optimized the database queries by adding indexes to frequently searched fields and adjusted the code to minimize redundant calculations. Additionally, I implemented batch processing to handle multiple records simultaneously instead of processing each one individually. As a result, the pipeline’s processing time decreased by nearly 50%, which allowed us to deliver data insights faster to stakeholders. This experience underscored the importance of detailed analysis and incremental improvements when addressing performance issues.

Conclusion

Securing a Walgreens SDE III role requires not only deep technical expertise but also the strategic thinking to design scalable, high-performing systems. The questions we’ve explored offer targeted preparation across critical areas like data structures, algorithms, and system design, empowering you to tackle Walgreens’ interview challenges confidently. By honing these skills, you’re building the capability to contribute meaningfully to complex projects, delivering efficient, reliable solutions that meet the rigorous demands of a senior engineering role.

Preparation for the Walgreens SDE III interview means immersing yourself in scenarios that reveal your problem-solving strengths and ability to manage real-world challenges. Mastering these questions will refine your technical acumen and sharpen your adaptability in handling large-scale applications, high-concurrency demands, and intricate debugging tasks. With the insights gained from these questions, you’ll enter your interview equipped not only to answer tough questions but to demonstrate the impact-driven mindset and technical leadership that Walgreens values in its SDE III engineers.